1 Data overview
2 NMDS By Grass Host
3 Subset by Grass Host
- 3.1 Influence of discrete latitudinal bins
- 3.2 Influence of discrete longitudinal gradients

opts <- options(knitr.kable.NA = "")

Set-up

0.1 Import clean OTU tables

0.2 Ordinate all grasses and by-host (trying k 1-7)

0.3 QC, stress check and validation of ordinations

ord_checkfit_df = data.frame()

for (name in comm.ord.names.by.grass) {
  
  output = extract.nmds.checkfit(get(name))

  ord_checkfit_df = rbind(ord_checkfit_df, output)

}

# Ideal stress value is < 0.05 (but k > 5 makes interpretation difficult)
    # Higher than 0.2 is poor (risks for false interpretation).
    # 0.1 - 0.2 is fair (some distances can be misleading for interpretation).
    # 0.05 - 0.1 is good (can be confident in inferences from plot).
    # Less than 0.05 is excellent (this can be rare).


ord_checkfit_df$stress<-round(ord_checkfit_df$stress,3)
ord_checkfit_df$nonmetric.fit.r2<-round(ord_checkfit_df$nonmetric.fit.r2,3)
ord_checkfit_df$linear.fit.r2<-round(ord_checkfit_df$linear.fit.r2,3)


# Final choices on k values

## AllG
  subset(ord_checkfit_df,ord_checkfit_df$input_data==comm.names.by.grass[1])
  subset(ord_checkfit_df,ord_checkfit_df$input_data==comm.names.by.grass[2])
  
  # flipping x axis coords for ease of interpretation
    # Fun.AllG.k5.ord$points[,1]<-Fun.AllG.k5.ord$points[,1]*-1 # already done
  
  saveRDS(Bac.AllG.k5.ord, file="../processed_data/clean_rds_saves/Bac.AllG.k5.ord.rds")
  saveRDS(Fun.AllG.k5.ord, file="../processed_data/clean_rds_saves/Fun.AllG.k5.ord.rds")

## ANGE
  subset(ord_checkfit_df,ord_checkfit_df$input_data==comm.names.by.grass[3])
  subset(ord_checkfit_df,ord_checkfit_df$input_data==comm.names.by.grass[4])
  
  #flip x coord of ITS Fun.ANGE.k5.ord
     # Fun.ANGE.k5.ord$points[,1]<-Fun.ANGE.k5.ord$points[,1]*-1 # already done
  
  saveRDS(Bac.ANGE.k5.ord, file="../processed_data/clean_rds_saves/Bac.ANGE.k5.ord.rds")
  saveRDS(Fun.ANGE.k5.ord, file="../processed_data/clean_rds_saves/Fun.ANGE.k5.ord.rds")

## BOER
  saveRDS(Bac.BOER.k5.ord, file="../processed_data/clean_rds_saves/Bac.BOER.k5.ord.rds")
  saveRDS(Fun.BOER.k5.ord, file="../processed_data/clean_rds_saves/Fun.BOER.k5.ord.rds")

## BOGR
  subset(ord_checkfit_df,ord_checkfit_df$input_data==comm.names.by.grass[7])
  subset(ord_checkfit_df,ord_checkfit_df$input_data==comm.names.by.grass[8])
  
  saveRDS(Bac.BOGR.k5.ord, file="../processed_data/clean_rds_saves/Bac.BOGR.k5.ord.rds")
  saveRDS(Fun.BOGR.k5.ord, file="../processed_data/clean_rds_saves/Fun.BOGR.k5.ord.rds")

## BUDA
  subset(ord_checkfit_df,ord_checkfit_df$input_data==comm.names.by.grass[9])
  subset(ord_checkfit_df,ord_checkfit_df$input_data==comm.names.by.grass[10])
  
  # flipping y axis coords for ease of interpretation
    # Fun.BUDA.k6.ord$points[,2]<-Fun.BUDA.k6.ord$points[,2]*-1 # already done
  
  saveRDS(Bac.BUDA.k6.ord, file="../processed_data/clean_rds_saves/Bac.BUDA.k6.ord.rds")
  saveRDS(Fun.BUDA.k6.ord, file="../processed_data/clean_rds_saves/Fun.BUDA.k6.ord.rds")

## SCSC
  subset(ord_checkfit_df,ord_checkfit_df$input_data==comm.names.by.grass[11])
  subset(ord_checkfit_df,ord_checkfit_df$input_data==comm.names.by.grass[12])
  
  saveRDS(Bac.SCSC.k5.ord, file="../processed_data/clean_rds_saves/Bac.SCSC.k5.ord.rds")
  saveRDS(Fun.SCSC.k5.ord, file="../processed_data/clean_rds_saves/Fun.SCSC.k5.ord.rds")

0.4 Set-up metadata

Available metadata:

##  [1] "SampleID"       "ITS_ID"         "sample_name"   
##  [4] "Replicate"      "Site"           "Site_Grass"    
##  [7] "Site.Rep"       "Grass"          "Grass_Rep"     
## [10] "Edge"           "SiteName"       "MAT_degC"      
## [13] "MAP_mm"         "CollectionDate" "Latitude"      
## [16] "Longitude"      "Elevation_m"    "Grassland"     
## [19] "Gradient"       "Bin"            "julianday"     
## [22] "coll_GDD"       "GDD30yr"        "GDD3yr"        
## [25] "GDD2015"        "ppt30yr"        "ppt3yr"        
## [28] "ppt2015"        "soil_moisture"  "GWC"           
## [31] "perc_moisture"  "pH"             "phos"          
## [34] "ammonium"       "nitrate"        "SOM"           
## [37] "herbivory_perc" "avg_SLA"        "avg_SRL"

1 Data overview

All EDGE samples excluded
OTU tables rarefied (16S: 9,048 & ITS: 10,116 seqs/sample)
‘Taxa’ are OTUs

AllG (All Grasses)

Bacterial & Archaeal Dataset:

Bac.AllG

## phyloseq-class experiment-level object
## otu_table()   OTU Table:         [ 23156 taxa and 533 samples ]
## sample_data() Sample Data:       [ 533 samples by 39 sample variables ]
## tax_table()   Taxonomy Table:    [ 23156 taxa by 6 taxonomic ranks ]

Fungal Dataset:

Fun.AllG

## phyloseq-class experiment-level object
## otu_table()   OTU Table:         [ 6749 taxa and 509 samples ]
## sample_data() Sample Data:       [ 509 samples by 39 sample variables ]
## tax_table()   Taxonomy Table:    [ 6749 taxa by 7 taxonomic ranks ]

ANGE (Andropogon gerardii / Big bluestem)

Bacterial & Archaeal Dataset:

Bac.ANGE

## phyloseq-class experiment-level object
## otu_table()   OTU Table:         [ 14268 taxa and 106 samples ]
## sample_data() Sample Data:       [ 106 samples by 39 sample variables ]
## tax_table()   Taxonomy Table:    [ 14268 taxa by 6 taxonomic ranks ]

Fungal Dataset:

Fun.ANGE

## phyloseq-class experiment-level object
## otu_table()   OTU Table:         [ 2985 taxa and 98 samples ]
## sample_data() Sample Data:       [ 98 samples by 39 sample variables ]
## tax_table()   Taxonomy Table:    [ 2985 taxa by 7 taxonomic ranks ]

BOER (B. eriopoda / Black grama)

Bacterial & Archaeal Dataset:

Bac.BOER

## phyloseq-class experiment-level object
## otu_table()   OTU Table:         [ 8374 taxa and 84 samples ]
## sample_data() Sample Data:       [ 84 samples by 39 sample variables ]
## tax_table()   Taxonomy Table:    [ 8374 taxa by 6 taxonomic ranks ]

Fungal Dataset:

Fun.BOER

## phyloseq-class experiment-level object
## otu_table()   OTU Table:         [ 1662 taxa and 78 samples ]
## sample_data() Sample Data:       [ 78 samples by 39 sample variables ]
## tax_table()   Taxonomy Table:    [ 1662 taxa by 7 taxonomic ranks ]

BOGR (B. gracilis / Blue grama)

Bacterial & Archaeal Dataset:

Bac.BOGR

## phyloseq-class experiment-level object
## otu_table()   OTU Table:         [ 10108 taxa and 86 samples ]
## sample_data() Sample Data:       [ 86 samples by 39 sample variables ]
## tax_table()   Taxonomy Table:    [ 10108 taxa by 6 taxonomic ranks ]

Fungal Dataset:

Fun.BOGR

## phyloseq-class experiment-level object
## otu_table()   OTU Table:         [ 2668 taxa and 92 samples ]
## sample_data() Sample Data:       [ 92 samples by 39 sample variables ]
## tax_table()   Taxonomy Table:    [ 2668 taxa by 7 taxonomic ranks ]

BUDA (Bouteloua dactyloides / Buffalograss)

Bacterial & Archaeal Dataset:

Bac.BUDA

## phyloseq-class experiment-level object
## otu_table()   OTU Table:         [ 11048 taxa and 83 samples ]
## sample_data() Sample Data:       [ 83 samples by 39 sample variables ]
## tax_table()   Taxonomy Table:    [ 11048 taxa by 6 taxonomic ranks ]

Fungal Dataset:

Fun.BUDA

## phyloseq-class experiment-level object
## otu_table()   OTU Table:         [ 2369 taxa and 80 samples ]
## sample_data() Sample Data:       [ 80 samples by 39 sample variables ]
## tax_table()   Taxonomy Table:    [ 2369 taxa by 7 taxonomic ranks ]

SCSC (Schizachyrium scoparium / Little bluestem)

Bacterial & Archaeal Dataset:

Bac.SCSC

## phyloseq-class experiment-level object
## otu_table()   OTU Table:         [ 17568 taxa and 174 samples ]
## sample_data() Sample Data:       [ 174 samples by 39 sample variables ]
## tax_table()   Taxonomy Table:    [ 17568 taxa by 6 taxonomic ranks ]

Fungal Dataset:

Fun.SCSC

## phyloseq-class experiment-level object
## otu_table()   OTU Table:         [ 4347 taxa and 161 samples ]
## sample_data() Sample Data:       [ 161 samples by 39 sample variables ]
## tax_table()   Taxonomy Table:    [ 4347 taxa by 7 taxonomic ranks ]

2 NMDS By Grass Host

- ANGE (Andropogon gerardii / Big bluestem)
- SCSC (Schizachyrium scoparium / Little bluestem)
- BOER (B. eriopoda / Black grama)
- BOGR (B. gracilis / Blue grama)
- BUDA (Bouteloua dactyloides / Buffalograss)

NMDS plots of all samples, colored by grass host

knitr::kable((as.data.frame(subset(adonis.out.array_predGrass, 
                    comm==comm.names.by.grass[1])))[,1:5], "simple", 
             digits =c (0,1,3,2,3), 
             align = 'c')

	Df	SumOfSqs	R2	F	Pr(>F)
Bac.AllG.Grass	4	18.6	0.132	20.12	0.001
Bac.AllG.Residual	528	122.2	0.868
Bac.AllG.Total	532	140.8	1.000

knitr::kable((as.data.frame(subset(adonis.out.array_predGrass, 
                    comm==comm.names.by.grass[2])))[,1:5], "simple", 
             digits =c (0,1,3,2,3), 
             align = 'c')

	Df	SumOfSqs	R2	F	Pr(>F)
Fun.AllG.Grass	4	13.2	0.064	8.67	0.001
Fun.AllG.Residual	504	192.5	0.936
Fun.AllG.Total	508	205.7	1.000

3 Subset by Grass Host

3.1 Influence of discrete latitudinal bins

AllG (All Grasses)

NMDS plots of all samples, colored by latitudinal bin

knitr::kable((as.data.frame(subset(adonis.out.array_predBin, 
                    comm==comm.names.by.grass[1])))[,1:5], "simple", 
             digits =c (0,1,3,2,3), 
             align = 'c')

	Df	SumOfSqs	R2	F	Pr(>F)
Bac.AllG.Bin	3	7.9	0.056	10.48	0.001
Bac.AllG.Residual	529	132.9	0.944
Bac.AllG.Total	532	140.8	1.000

knitr::kable((as.data.frame(subset(adonis.out.array_predBin, 
                    comm==comm.names.by.grass[2])))[,1:5], "simple", 
             digits =c (0,1,3,2,3), 
             align = 'c')

	Df	SumOfSqs	R2	F	Pr(>F)
Fun.AllG.Bin	3	11.3	0.055	9.79	0.001
Fun.AllG.Residual	505	194.4	0.945
Fun.AllG.Total	508	205.7	1.000

ANGE (Andropogon gerardii / Big bluestem)

NMDS plots of ANGE host-associated samples, colored by latitudinal bin

knitr::kable((as.data.frame(subset(adonis.out.array_predBin, 
                    comm==comm.names.by.grass[3])))[,1:5], "simple", 
             digits =c (0,1,3,2,3), 
             align = 'c')

	Df	SumOfSqs	R2	F	Pr(>F)
Bac.ANGE.Bin	3	4.4	0.157	6.33	0.001
Bac.ANGE.Residual	102	23.8	0.843
Bac.ANGE.Total	105	28.2	1.000

knitr::kable((as.data.frame(subset(adonis.out.array_predBin, 
                    comm==comm.names.by.grass[4])))[,1:5], "simple", 
             digits =c (0,1,3,2,3), 
             align = 'c')

	Df	SumOfSqs	R2	F	Pr(>F)
Fun.ANGE.Bin	3	6.4	0.174	6.59	0.001
Fun.ANGE.Residual	94	30.6	0.826
Fun.ANGE.Total	97	37.0	1.000

BOER (B. eriopoda / Black grama)

NMDS plots of BOER host-associated samples, colored by latitudinal bin

knitr::kable((as.data.frame(subset(adonis.out.array_predBin, 
                    comm==comm.names.by.grass[5])))[,1:5], "simple", 
             digits =c (0,1,3,2,3), 
             align = 'c')

	Df	SumOfSqs	R2	F	Pr(>F)
Bac.BOER.Bin	3	6	0.354	14.58	0.001
Bac.BOER.Residual	80	11	0.646
Bac.BOER.Total	83	17	1.000

knitr::kable((as.data.frame(subset(adonis.out.array_predBin, 
                    comm==comm.names.by.grass[6])))[,1:5], "simple", 
             digits =c (0,1,3,2,3), 
             align = 'c')

	Df	SumOfSqs	R2	F	Pr(>F)
Fun.BOER.Bin	3	5.4	0.194	5.94	0.001
Fun.BOER.Residual	74	22.3	0.806
Fun.BOER.Total	77	27.7	1.000

BOGR (B. gracilis / Blue grama)

NMDS plots of BOGR host-associated samples, colored by latitudinal bin

knitr::kable((as.data.frame(subset(adonis.out.array_predBin, 
                    comm==comm.names.by.grass[7])))[,1:5], "simple", 
             digits =c (0,1,3,2,3), 
             align = 'c')

	Df	SumOfSqs	R2	F	Pr(>F)
Bac.BOGR.Bin	3	2.9	0.168	5.51	0.001
Bac.BOGR.Residual	82	14.3	0.832
Bac.BOGR.Total	85	17.2	1.000

knitr::kable((as.data.frame(subset(adonis.out.array_predBin, 
                    comm==comm.names.by.grass[8])))[,1:5], "simple", 
             digits =c (0,1,3,2,3), 
             align = 'c')

	Df	SumOfSqs	R2	F	Pr(>F)
Fun.BOGR.Bin	3	4.0	0.126	4.24	0.001
Fun.BOGR.Residual	88	27.7	0.874
Fun.BOGR.Total	91	31.8	1.000

BUDA (Bouteloua dactyloides / Buffalograss)

NMDS plots of BUDA host-associated samples, colored by latitudinal bin

knitr::kable((as.data.frame(subset(adonis.out.array_predBin, 
                    comm==comm.names.by.grass[9])))[,1:5], "simple", 
             digits =c (0,1,3,2,3), 
             align = 'c')

	Df	SumOfSqs	R2	F	Pr(>F)
Bac.BUDA.Bin	3	2.6	0.171	5.45	0.001
Bac.BUDA.Residual	79	12.4	0.829
Bac.BUDA.Total	82	15.0	1.000

knitr::kable((as.data.frame(subset(adonis.out.array_predBin, 
                    comm==comm.names.by.grass[10])))[,1:5], "simple", 
             digits =c (0,1,3,2,3), 
             align = 'c')

	Df	SumOfSqs	R2	F	Pr(>F)
Fun.BUDA.Bin	3	4.3	0.14	4.11	0.001
Fun.BUDA.Residual	76	26.5	0.86
Fun.BUDA.Total	79	30.8	1.00

SCSC (Schizachyrium scoparium / Little bluestem)

NMDS plots of SCSC host-associated samples, colored by latitudinal bin

knitr::kable((as.data.frame(subset(adonis.out.array_predBin, 
                    comm==comm.names.by.grass[11])))[,1:5], "simple", 
             digits =c (0,1,3,2,3), 
             align = 'c')

	Df	SumOfSqs	R2	F	Pr(>F)
Bac.SCSC.Bin	3	4.0	0.09	5.58	0.001
Bac.SCSC.Residual	170	40.8	0.91
Bac.SCSC.Total	173	44.8	1.00

knitr::kable((as.data.frame(subset(adonis.out.array_predBin, 
                    comm==comm.names.by.grass[12])))[,1:5], "simple", 
             digits =c (0,1,3,2,3), 
             align = 'c')

	Df	SumOfSqs	R2	F	Pr(>F)
Fun.SCSC.Bin	3	5.3	0.081	4.62	0.001
Fun.SCSC.Residual	157	60.0	0.919
Fun.SCSC.Total	160	65.3	1.000

3.2 Influence of discrete longitudinal gradients

AllG (All Grasses)

NMDS plots of all samples, colored by longitudinal gradient

knitr::kable((as.data.frame(subset(adonis.out.array_predGradient, 
                    comm==comm.names.by.grass[1])))[,1:5], "simple", 
             digits =c (0,1,3,2,3), 
             align = 'c')

	Df	SumOfSqs	R2	F	Pr(>F)
Bac.AllG.Gradient	2	17.5	0.124	37.57	0.001
Bac.AllG.Residual	530	123.3	0.876
Bac.AllG.Total	532	140.8	1.000

knitr::kable((as.data.frame(subset(adonis.out.array_predGradient, 
                    comm==comm.names.by.grass[2])))[,1:5], "simple", 
             digits =c (0,1,3,2,3), 
             align = 'c')

	Df	SumOfSqs	R2	F	Pr(>F)
Fun.AllG.Gradient	2	10.4	0.05	13.45	0.001
Fun.AllG.Residual	506	195.3	0.95
Fun.AllG.Total	508	205.7	1.00

ANGE (Andropogon gerardii / Big bluestem)

NMDS plots of ANGE host-associated samples, colored by longitudinal gradient

knitr::kable((as.data.frame(subset(adonis.out.array_predGradient, 
                    comm==comm.names.by.grass[3])))[,1:5], "simple", 
             digits =c (0,1,3,2,3), 
             align = 'c')

	Df	SumOfSqs	R2	F	Pr(>F)
Bac.ANGE.Gradient	1	2.8	0.1	11.62	0.001
Bac.ANGE.Residual	104	25.4	0.9
Bac.ANGE.Total	105	28.2	1.0

knitr::kable((as.data.frame(subset(adonis.out.array_predGradient, 
                    comm==comm.names.by.grass[4])))[,1:5], "simple", 
             digits =c (0,1,3,2,3), 
             align = 'c')

	Df	SumOfSqs	R2	F	Pr(>F)
Fun.ANGE.Gradient	1	1.8	0.05	5.04	0.001
Fun.ANGE.Residual	96	35.2	0.95
Fun.ANGE.Total	97	37.0	1.00

BOER (B. eriopoda / Black grama)

NMDS plots of BOER host-associated samples, colored by longitudinal gradient

[NA on stats here - only 1 factor level]

# knitr::kable((as.data.frame(subset(adonis.out.array_predGradient, 
#                     comm==comm.names.by.grass[5])))[,1:5], "simple", 
#              digits =c (0,1,3,2,3), 
#              align = 'c')

# knitr::kable((as.data.frame(subset(adonis.out.array_predGradient, 
#                     comm==comm.names.by.grass[6])))[,1:5], "simple", 
#              digits =c (0,1,3,2,3), 
#              align = 'c')

BOGR (B. gracilis / Blue grama)

NMDS plots of BOGR host-associated samples, colored by longitudinal gradient

knitr::kable((as.data.frame(subset(adonis.out.array_predGradient, 
                    comm==comm.names.by.grass[7])))[,1:5], "simple", 
             digits =c (0,1,3,2,3), 
             align = 'c')

	Df	SumOfSqs	R2	F	Pr(>F)
Bac.BOGR.Gradient	2	2.6	0.152	7.45	0.001
Bac.BOGR.Residual	83	14.5	0.848
Bac.BOGR.Total	85	17.2	1.000

knitr::kable((as.data.frame(subset(adonis.out.array_predGradient, 
                    comm==comm.names.by.grass[8])))[,1:5], "simple", 
             digits =c (0,1,3,2,3), 
             align = 'c')

	Df	SumOfSqs	R2	F	Pr(>F)
Fun.BOGR.Gradient	2	3.5	0.109	5.46	0.001
Fun.BOGR.Residual	89	28.3	0.891
Fun.BOGR.Total	91	31.8	1.000

BUDA (Bouteloua dactyloides / Buffalograss)

NMDS plots of BUDA host-associated samples, colored by longitudinal gradient

knitr::kable((as.data.frame(subset(adonis.out.array_predGradient, 
                    comm==comm.names.by.grass[9])))[,1:5], "simple", 
             digits =c (0,1,3,2,3), 
             align = 'c')

	Df	SumOfSqs	R2	F	Pr(>F)
Bac.BUDA.Gradient	2	2.6	0.172	8.3	0.001
Bac.BUDA.Residual	80	12.4	0.828
Bac.BUDA.Total	82	15.0	1.000

knitr::kable((as.data.frame(subset(adonis.out.array_predGradient, 
                    comm==comm.names.by.grass[10])))[,1:5], "simple", 
             digits =c (0,1,3,2,3), 
             align = 'c')

	Df	SumOfSqs	R2	F	Pr(>F)
Fun.BUDA.Gradient	2	3.0	0.097	4.14	0.001
Fun.BUDA.Residual	77	27.8	0.903
Fun.BUDA.Total	79	30.8	1.000

SCSC (Schizachyrium scoparium / Little bluestem)

NMDS plots of SCSC host-associated samples, colored by longitudinal gradient

knitr::kable((as.data.frame(subset(adonis.out.array_predGradient, 
                    comm==comm.names.by.grass[11])))[,1:5], "simple", 
             digits =c (0,1,3,2,3), 
             align = 'c')

	Df	SumOfSqs	R2	F	Pr(>F)
Bac.SCSC.Gradient	2	5.9	0.131	12.93	0.001
Bac.SCSC.Residual	171	38.9	0.869
Bac.SCSC.Total	173	44.8	1.000

knitr::kable((as.data.frame(subset(adonis.out.array_predGradient, 
                    comm==comm.names.by.grass[12])))[,1:5], "simple", 
             digits =c (0,1,3,2,3), 
             align = 'c')

	Df	SumOfSqs	R2	F	Pr(>F)
Fun.SCSC.Gradient	2	3.4	0.052	4.37	0.001
Fun.SCSC.Residual	158	61.8	0.948
Fun.SCSC.Total	160	65.3	1.000

KSU paired amplicon community ordinations

Comparative NMDS plots of 16S and ITS data (EDGE samples excluded), updated to include adonis2 analyses (permANOVA by metadata)

Kelli Feeser

2023-10-02

Set-up

0.1 Import clean OTU tables

0.2 Ordinate all grasses and by-host (trying k 1-7)

0.3 QC, stress check and validation of ordinations

0.4 Set-up metadata

adonis2 tables

by grass

by Bin

by Gradient

save adonis2 outputs

Generate plots for later viewing

color by grass

color by bin

color by gradient

1 Data overview

AllG (All Grasses)

ANGE (Andropogon gerardii / Big bluestem)

BOER (B. eriopoda / Black grama)

BOGR (B. gracilis / Blue grama)

BUDA (Bouteloua dactyloides / Buffalograss)

SCSC (Schizachyrium scoparium / Little bluestem)

2 NMDS By Grass Host

3 Subset by Grass Host

3.1 Influence of discrete latitudinal bins

AllG (All Grasses)

ANGE (Andropogon gerardii / Big bluestem)

BOER (B. eriopoda / Black grama)

BOGR (B. gracilis / Blue grama)

BUDA (Bouteloua dactyloides / Buffalograss)

SCSC (Schizachyrium scoparium / Little bluestem)

3.2 Influence of discrete longitudinal gradients

AllG (All Grasses)

ANGE (Andropogon gerardii / Big bluestem)

BOER (B. eriopoda / Black grama)

BOGR (B. gracilis / Blue grama)

BUDA (Bouteloua dactyloides / Buffalograss)

SCSC (Schizachyrium scoparium / Little bluestem)